4 Genomic data

4.1 Read duplicates

all_data %>%
    select(dataset,Extraction,duplicates,Taxon) %>%
    unique() %>%
    group_by(Taxon,Extraction) %>%
    summarise(value = sprintf("%.1f±%.1f", mean(duplicates), sd(duplicates))) %>%
    pivot_wider(names_from = Extraction, values_from = value) %>%
    tt(caption = "Mean and standard deviation of fraction of duplicated reads")

Mean and standard deviation of fraction of duplicated reads

Taxon	DREX	EHEX	ZYMO
Amphibian	0.2±0.2	0.2±0.2	0.3±0.2
Bird	0.9±0.1	0.6±0.4	0.8±0.3
Control	0.9±0.0	1.0±0.0	1.0±0.0
Mammal	0.2±0.1	0.2±0.2	0.4±0.4
Reptile	0.3±0.3	0.4±0.4	0.5±0.4

all_data %>%
    select(dataset,Extraction,duplicates,Taxon) %>%
    unique() %>%
    mutate(Taxon=factor(Taxon,levels=c("Amphibian","Reptile","Mammal","Bird","Control"))) %>%
    mutate(Extraction=factor(Extraction,levels=c("ZYMO","DREX","EHEX"))) %>%
    ggplot(aes(x=Extraction,y=duplicates))+ 
        geom_boxplot() + 
        facet_grid(. ~ Taxon, scales = "free") +
        labs(y="Duplication rate",x="Extraction method")

all_data %>%
    select(dataset,Sample,Species,Extraction,duplicates,Taxon) %>%
    filter(Taxon != "Control") %>%
    lmerTest::lmer(duplicates ~ Extraction + (1 | Sample) + (1 | Species), data = ., REML = FALSE) %>%
    broom.mixed::tidy() %>%
    tt()

effect	group	term	estimate	std.error	statistic	df	p.value
fixed	NA	(Intercept)	0.39361067	0.07257915	5.423192	14.33359	8.267782e-05
fixed	NA	ExtractionEHEX	-0.05339005	0.03918499	-1.362513	160.73104	1.749423e-01
fixed	NA	ExtractionZYMO	0.09279626	0.03900562	2.379048	160.72377	1.853073e-02
ran_pars	Sample	sd__(Intercept)	0.00000000	NA	NA	NA	NA
ran_pars	Species	sd__(Intercept)	0.23148121	NA	NA	NA	NA
ran_pars	Residual	sd__Observation	0.21005171	NA	NA	NA	NA

4.2 Depth of coverage

all_data %>%
    select(dataset,Extraction,coverage_depth,Taxon) %>%
    unique() %>%
    group_by(Taxon,Extraction) %>%
    summarise(value = sprintf("%.1f±%.1f", mean(coverage_depth), sd(coverage_depth))) %>%
    pivot_wider(names_from = Extraction, values_from = value) %>%
    tt(caption = "Mean and standard deviation of fraction of duplicated reads")

Mean and standard deviation of fraction of duplicated reads

Taxon	DREX	EHEX	ZYMO
Amphibian	0.0±0.0	0.0±0.0	0.0±0.0
Bird	0.6±0.5	0.8±0.8	0.4±0.8
Control	0.0±0.0	0.0±0.0	0.0±0.0
Mammal	0.3±0.4	0.5±1.0	0.7±1.2
Reptile	0.1±0.1	0.1±0.1	0.2±0.3

all_data %>%
    select(dataset,Extraction,coverage_depth,Taxon) %>%
    unique() %>%
    mutate(Taxon=factor(Taxon,levels=c("Amphibian","Reptile","Mammal","Bird","Control"))) %>%
    mutate(Extraction=factor(Extraction,levels=c("ZYMO","DREX","EHEX"))) %>%
    ggplot(aes(x=Extraction,y=coverage_depth))+ 
        geom_boxplot() + 
        facet_grid(. ~ Taxon, scales = "free") +
        labs(y="Depth of coverage",x="Extraction method")

all_data %>%
    select(dataset,Sample,Species,Extraction,coverage_depth,Taxon) %>%
    unique() %>%
    filter(Taxon != "Control") %>%
    lmerTest::lmer(coverage_depth ~ Extraction + (1 | Sample) + (1 | Species), data = ., REML = FALSE) %>%
    broom.mixed::tidy() %>%
    tt()

effect	group	term	estimate	std.error	statistic	df	p.value
fixed	NA	(Intercept)	0.23933333	0.1331063	1.7980622	20.58494	0.0868510
fixed	NA	ExtractionEHEX	0.08700000	0.1144640	0.7600640	48.00000	0.4509335
fixed	NA	ExtractionZYMO	0.08979167	0.1144640	0.7844531	48.00000	0.4366287
ran_pars	Sample	sd__(Intercept)	0.40863458	NA	NA	NA	NA
ran_pars	Species	sd__(Intercept)	0.22473121	NA	NA	NA	NA
ran_pars	Residual	sd__Observation	0.39651507	NA	NA	NA	NA

4.3 Breadth of coverage

all_data %>%
    select(dataset,Extraction,coverage_breadth,Taxon) %>%
    unique() %>%
    group_by(Taxon,Extraction) %>%
    summarise(value = sprintf("%.1f±%.1f", mean(coverage_breadth), sd(coverage_breadth))) %>%
    pivot_wider(names_from = Extraction, values_from = value) %>%
    tt(caption = "Mean and standard deviation of depth of host genome coverage")

Mean and standard deviation of depth of host genome coverage

Taxon	DREX	EHEX	ZYMO
Amphibian	0.0±0.0	0.0±0.0	0.0±0.0
Bird	3.2±4.4	8.9±13.9	0.6±0.5
Control	0.0±0.0	0.0±0.0	0.0±0.0
Mammal	10.2±16.4	15.1±26.4	5.7±5.9
Reptile	3.0±5.4	2.9±3.7	4.9±7.5

all_data %>%
    select(dataset,Extraction,coverage_breadth,Taxon) %>%
    unique() %>%
    mutate(Taxon=factor(Taxon,levels=c("Amphibian","Reptile","Mammal","Bird","Control"))) %>%
    mutate(Extraction=factor(Extraction,levels=c("ZYMO","DREX","EHEX"))) %>%
    ggplot(aes(x=Extraction,y=coverage_breadth))+ 
        geom_boxplot() + 
        facet_grid(. ~ Taxon, scales = "free") +
        labs(y="Breadth of coverage",x="Extraction method")

all_data %>%
    select(dataset,Extraction,Sample,Species,coverage_breadth,Taxon) %>%
    unique() %>%
    filter(Taxon != "Control") %>%
    lmerTest::lmer(coverage_breadth ~ Extraction + (1 | Sample) + (1 | Species), data = ., REML = FALSE) %>%
    broom.mixed::tidy() %>%
    tt()

effect	group	term	estimate	std.error	statistic	df	p.value
fixed	NA	(Intercept)	4.100417	2.252826	1.8201211	20.82558	0.08314479
fixed	NA	ExtractionEHEX	2.617500	1.956426	1.3378986	48.00000	0.18723379
fixed	NA	ExtractionZYMO	-1.301250	1.956426	-0.6651158	48.00000	0.50915975
ran_pars	Sample	sd__(Intercept)	7.118462	NA	NA	NA	NA
ran_pars	Species	sd__(Intercept)	3.549767	NA	NA	NA	NA
ran_pars	Residual	sd__Observation	6.777259	NA	NA	NA	NA